In our previous blog article, we explored the important role of extracellular vesicles (EVs) as ‘secret messengers’ that help to regulate human health and disease, and their promising applications as disease biomarkers, therapeutic targets or drug delivery platforms.
However, EVs present significant technical challenges when it comes to analyzing their proteome, and many proteomics technologies struggle to accurately study their contents. This is particularly true for the smallest subset of EVs, exosomes, which are just 30-150 nm in diameter.
In this article, we explore how recent advances in mass spectrometry (MS) proteomics can now offer a solution to these challenges, providing robust, scalable, deep analysis of the EV proteome.
Conventional plasma or serum proteomic analysis provides information on everything in the sample, from circulating cells and cleaved membrane proteins to EVs. This makes it hard to dissect the information and ensure your biomarker or signal is specific to the disease you are studying. But because the contents of EVs are representative of their parental cell, it’s clear exactly where these biomarkers are coming from.
Using size-exclusion chromatography (SEC) – a highly sensitive technique that separates EVs by size – it is possible to home in on a subpopulation of specific vesicles such as exosomes for proteomic analysis.
At Biognosys, we have combined the power of SEC with our highly sensitive TrueDiscovery™ mass spectrometry platform to create a novel automated and scalable workflow for EV analysis. This approach enables deep identification and quantification of thousands of proteins from just 200 µl of plasma or serum.
In collaboration with the University of Zurich and Stanford University School of Medicine we used our SEC-DIA-MS exosome workflow to analyze serum and plasma samples from melanoma patients and matched controls. The results were presented at the recent American Association for Cancer Research 2023 meeting.
Overall, our team was able to quantify 2,242 exosome-associated proteins, achieving a 2.5-fold increase in the depth of proteome analysis compared with previous melanoma studies (Figure 1). By comparing native, depleted and EV-enriched blood from the same donors, we were able to better understand exosome enrichment efficiency and validate well-known EV markers, such as CD9, CD63, CD81, PDCD6IP, and TSG101.
Figure 1: Overall protein identification
(A) Overlap of identified proteins across 3 blood compartments. (B) Comparison of number of identified protein groups in plasma-derived EVs in previous melanoma studies and the present study.
We found that EV samples appear to contain a high number of intact membrane proteins and proteins associated with T cell biology, highlighting the unique composition of the EV proteome. We also discovered that known melanoma markers, such as MCAM and TGFBI, were upregulated in melanoma plasma-derived EV samples but not in depleted plasma samples, emphasizing the vital information gained from EV analysis.
One important takeaway for future studies is that plasma samples seem to provide cleaner exosome data compared to serum, with better separation between cancer patients and healthy controls. It is also worth noting that melanoma is known to exhibit an increased secretion of EVs, which are specific to the disease rather than systemic. With this knowledge in hand, it could be possible to use our SEC-DIA-MS workflow to search for both predictive and treatment EV biomarkers that offer better patient stratification in the future.
It’s clear to see how SEC-DIA-MS could aid in the search for more specific biomarkers of disease. But this is just the beginning.
EV analysis is a hot area of biomarker research in biofluids, with many different approaches and technologies emerging in this space. For example, Mag-Net is another exosome enrichment approach based on magnetic microparticles that enables the quantification of the entire plasma EV proteome, offering a complementary approach to our own SEC-DIA-MS workflow that looks at specific subsets of vesicles.
In this rapidly evolving area, there is also an increasing need to find sensitive, cost-effective and high-throughput approaches suitable for clinical applications across a range of diseases. Large-scale, automated workflows for EV analysis, such as SEC-DIA-MS of blood and other sample types, could deepen our understanding of their role in health and disease and lead to the next generation of biomarkers, diagnostics and therapies.
For example, with our latest Sprectronaut 18 software update, protein identification can be increased even further, to around 3000 proteins in EV analysis. O nce any clinically relevant exosome biomarkers have been identified, our TrueSignature™ platform could be used to analyze them with custom targeted proteomics panels. Thanks to our GCP-complaint facility, these panels can then be integrated into clinical trials further down the line.
If you want to learn more about what our TrueDiscovery™ SEC-DIA-MS workflow can do for you, get in touch with our expert team. Stay tuned for Biognosys research updates in this field at the upcoming conferences.